Interference power measurement apparatus and method for space-time beam forming

ABSTRACT

A noise and interference power measurement apparatus for an antenna diversity system that services a plurality of users with an array antenna having a plurality of antenna elements. A channel estimator estimates a channel impulse response for a radio channel corresponding to a predetermined plurality of regularly spaced direction-of-arrival (DOA) values. A data estimator estimates the received data using a received signal and a system matrix. A quantizer quantizes the estimated data. An interference and noise calculator calculates noise vectors at the respective antenna elements by removing from the received signal an influence of the quantized data to which the system matrix is applied, calculates an estimated noise matrix at the plurality of antenna elements, calculates interference power by auto-correlating the estimated noise matrix, and calculates noise and interference power based on the interference power.

PRIORITY

This application claims the benefit under 35 U.S.C. §119(a) of anapplication entitled “Interference Power Measurement Apparatus andMethod for Space-Time Beam Forming” filed in the Korean IntellectualProperty Office on Jun. 10, 2004 and assigned Serial No. 2004-42746, theentire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to an array antenna system. Inparticular, the present invention relates to an apparatus and method formeasuring the interference power required for the calculation of spatialnoise and interference power for optimal beam forming in order totransmit and receive high-speed data at high quality in the arrayantenna system.

2. Description of the Related Art

The reception quality of radio signals is affected by many naturalphenomena. One natural phenomenon is temporal dispersion caused bysignals reflected off of obstacles in different positions in apropagation path before the signals arrive at a receiver. With theintroduction of digital coding in a wireless system, a temporaldispersion signal can be successfully restored using a Rake receiver orequalizer.

Another phenomenon called fast fading or Rayleigh fading, which isspatial dispersion caused by signals that are dispersed in a propagationpath by an object located a short distance from a transmitter or areceiver. If the signals received through different spaces, such asspatial signals, are combined in an inappropriate phase region, the sumof the received signals has a very low intensity, approaching zero. Thiscauses fading dips where the received signals substantially disappear,and the fading dip occurs as frequently as a length corresponding to awavelength.

A known method of removing fading is to provide an antenna diversitysystem to a receiver. The antenna diversity system typically includestwo or more spatially separated reception antennas. Signals received bythe respective antennas have low relation to one another with respect tofading, thereby reducing the possibility that the two antennas willsimultaneously generate the fading dips.

Another phenomenon that significantly affects radio transmission isinterference. Interference is defined as an undesired signal received ona desired signal channel. In a cellular radio system, interference isdirectly related to a requirement of communication capacity. Becauseradio spectrum is a limited resource, a radio frequency band given to acellular operator should be efficiently used.

Due to increasing use of cellular systems and their deployment overincreasing numbers of geographic locations, research is being conductedon an array antenna geometry connected to a beam former (BF) as a newscheme for increasing traffic capacity by removing any influences ofinterference and fading. Each antenna element forms a set of antennabeams. A signal transmitted from a transmitter is received by each ofthe antenna beams, and spatial signals experiencing different spatialchannels are maintained by individual angular information. The angularinformation is determined according to a phase difference betweendifferent signals. Direction estimation of a signal source is achievedby demodulating a received signal. The direction of a signal source isalso called the “Direction of Arrival (DOA).”

Estimation of DOAs is used to select an antenna beam for signaltransmission in a desired direction or to steer an antenna beam in adirection where a desired signal is received. A beam former estimatesthe steering vectors and DOAs for simultaneously detected multiplespatial signals, and determines beam-forming weight vectors from a setof the steering vectors. The beam-forming weight vectors are used forrestoring signals. Algorithms used for beam forming include MultipleSignal Classification (MUSIC), Estimation of Signal Parameters viaRotational Invariance Techniques (ESPRIT), Weighted Subspace Fitting(WSF), and Method of Direction Estimation (MODE).

An adaptive beam forming process depends on precise knowledge of thespatial channels. Therefore, adaptive beam forming can generally only beaccomplished after estimation of the spatial channels. This estimationis achieved through calculation of interference and noise power for aspace from a transmitter and a receiver. A known approach for estimationof noise power is to use forward error correction (FEC) decoding. Thismethod estimates the influence of interference by re-encoding previouslydetected and decoded data in the form of a reception signal matrix, andcomparing the signal matrix with a currently received signal.

Disadvantageously, however, the interference power measurement using FECdecoding increases structural complexity of a receiver and causes aconsiderable estimation delay. Because of the estimation delay, areceiver in the conventional array antenna system is limited to a lowmoving velocity and a Doppler level, and thus is restricted to a systemthat performs FEC decoding.

SUMMARY OF THE INVENTION

It is, therefore, an object of the present invention to provide anapparatus and method for measuring interference power using informationreceived such that it can be directly used at a receiver throughdemodulation and equalization, instead of using FEC decoding.

It is another object of the present invention to provide an apparatusand method for measuring interference power required for estimation of aradio channel for beam forming in an array antenna system.

It is a further object of the present invention to provide a beamforming apparatus and method capable of reducing the implementationcomplexity and efficiently using spatial diversity in a Time DomainDuplex (TDD) system like a Time Division Synchronous Code DivisionMultiple Access (TD-SCDMA) system.

According to one aspect of the present invention, there is provided anoise and interference power measurement apparatus for an antennadiversity system that services a plurality of users with an arrayantenna having a plurality of antenna elements. The apparatus comprisesa channel estimator for estimating a channel impulse response for aradio channel corresponding to a predetermined plurality of regularlyspaced direction-of-arrival (DOA) values; a data estimator forestimating received data using a received signal and a system matrixcomprising an allocated spreading code and the channel impulse response;a quantizer for quantizing the estimated data; and an interference andnoise calculator for calculating noise vectors at the respective antennaelements by removing from the received signal an influence of thequantized data to which the system matrix is applied, calculating anestimated noise matrix at the plurality of antenna elements, wherein theestimated noise matrix includes the noise vectors. The interference andnoise calculator calculates the interference power by auto-correlatingthe estimated noise matrix, and calculates the noise power based on thecalculated interference power.

According to another aspect of the present invention, there is provideda noise and interference power measurement method for an antennadiversity system that services a plurality of users with an arrayantenna having a plurality of antenna elements. The method comprises thesteps of estimating a channel impulse response for a radio channelcorresponding to a predetermined plurality of regularly spaceddirection-of-arrival (DOA) values; estimating received data using areceived signal and a system matrix including an allocated spreadingcode and the channel impulse response; quantizing the estimated data;calculating noise vectors at the respective antenna elements by removingfrom the received signal an influence of the quantized data to which thesystem matrix is applied; calculating an estimated noise matrix at theplurality of antenna elements, the estimated noise matrix including thenoise vectors, and calculating interference power by auto-correlatingthe estimated noise matrix; and calculating noise power based on thecalculated interference power.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will become more apparent from the following detaileddescription when taken in conjunction with the accompanying drawings inwhich:

FIG. 1 illustrates an example of a base station with an array antenna,which communicates with a plurality of mobile stations according to anembodiment of the present invention;

FIG. 2 is a polar plot illustrating spatial characteristics of beamforming for selecting a signal from one user according to an embodimentof the present invention;

FIG. 3 is a block diagram illustrating a structure of a receiver in anarray antenna system according to an embodiment of the presentinvention;

FIG. 4 is a block diagram illustrating a structure of a receiver in anarray antenna system according to another embodiment of the presentinvention; and

FIG. 5 is a flowchart illustrating a method for performing aninterference power measurement operation according to an embodiment ofthe present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary embodiments of the present invention will now be described indetail with reference to the accompanying drawings. In the followingdescription, a detailed description of known functions andconfigurations incorporated herein has been omitted for the sake ofclarity and conciseness.

Embodiments of the present invention described below determineinterference power without using forward error correction (FEC)decoding, in performing beam forming by estimating a spatial channel inan antenna diversity system. Specifically, an exemplary embodiment ofthe present invention reduces both the estimation delay andimplementation complexity using the information that can be directlyused at a receiver after a modulation and equation process, instead ofusing the FEC decoding.

For estimation of spatial channels, a reception side requires thearrangement of an array antenna having K_(a) antenna elements. Such anarray antenna serves as a spatial low-pass filter having a finitespatial resolution. The term “spatial low-pass filtering” refers to anoperation of dividing an incident wave (or impinging wave) of an arrayantenna into spatial signals that pass through different spatialregions. A receiver having the foregoing array antenna combines a finitenumber, N_(b), of spatial signals, through beam forming. As describedabove, the optimal beam forming requires information on DOAs and atemporal dispersion channel's impulse response for the DOAs. A value ofthe N_(b) cannot be greater than a value of the K_(a), and thusrepresents the number of resolvable spatial signals. The maximum value,max(N_(b)), of the N_(b) is fixed according to a geometry of the arrayantenna.

FIG. 1 illustrates an example of a base station (or a Node B) with anarray antenna, which communicates with a plurality of mobile stations(or user equipments). Referring to FIG. 1, a base station 10 has anarray antenna 20 comprised of 4 antenna elements. The base station 10has 5 users A, B, C, D and E located in its coverage area. A receiver 15selects signals from desired users from among the 5 users, by beamforming. Because the array antenna 20 of FIG. 1 has only 4 antennaelements, the receiver 15 restores signals from a maximum of 4 users, inthis case, signals from users A, B, D and E as illustrated, by beamforming.

FIG. 2 illustrates spatial characteristics of beam forming for selectinga signal from a user A, by way of example. As illustrated, a very highweight, or gain, is applied to a signal from a user A, while a gainapproximating zero is applied to the signals from the other users, Bthrough E.

A system model applied to an exemplary embodiment of the presentinvention will now be described.

A burst transmission frame of a radio communication system has burstsincluding two data carrying parts (also known as sub-frames or a halfburst) each comprised of N data symbols. Mid-ambles which are trainingsequences predefined between a transmitter and a receiver, having L_(m)chips, are included in each data carrying part so that the channelcharacteristics and interferences in a radio section can be measured.The radio communication system supports multiple access based onTransmit Diversity Code Division Multiple Access (TD-CDMA), and spreadseach data symbol using a Q-chip Orthogonal Variable Spreading Factor(OVSF) code, which is a user-specific CDMA code. In a radio environment,there are K users per cell and frequency band, and per time slot. As awhole, there are K_(i) inter-cell interferences.

A base station (or a Node B) uses an array antenna having K_(a) antennaelements. Assuming that a signal transmitted by a k^(th) user (k=1, . .. , K) is incident upon (impinges on) the array antenna in k_(d) ^((d))different directions, each of the directions is represented by acardinal identifier k_(d) (k_(d)=1, . . . , K_(d) ^((d))). Then, a phasefactor of a k_(d) ^(th) spatial signal which is incident upon the arrayantenna from a k^(th) user (i.e., a user #k) through a k_(a) ^(th)antenna element (such that an antenna element k_(a) (k_(a)=1, . . . ,K_(a))) is defined as $\begin{matrix}{{{\Psi\left( {k,k_{a},k_{d}} \right)} = {2\pi\quad{\frac{l^{(k_{a})}}{\lambda} \cdot {\cos\left( {\beta^{({k,k_{d}})} - \alpha^{(k_{a})}} \right)}}}},{k = {1\quad\ldots\quad K}},{k_{a} = {1\quad\ldots\quad K_{a}}},{k_{d} = {1\quad\ldots\quad K_{d}^{(k)}}}} & {{Equation}\quad(1)}\end{matrix}$

In Equation (1), α^((k) ^(a) ⁾ denotes an angle between a virtual lineconnecting antenna elements arranged with a predetermined distance fromeach other to a predetermined antenna array reference point and apredetermined reference line passing through the antenna array referencepoint, and its value is previously known to a receiver according to thegeometry of the array antenna. In addition, β^((k,k) ^(d) ⁾ denotes aDOA in radians, representing a direction of a k_(d) ^(th) spatial signalarriving from a user #k on the basis of the reference line, λ denotes awavelength of a carrier frequency, and l^((k) ^(a) ⁾ denotes a distancebetween a k_(a) ^(th) antenna element and the antenna array referencepoint.

For each DOA β^((k,k) ^(d) ⁾ of a desired signal associated with a user#k, a unique channel impulse response observable by a virtualunidirectional antenna located in the reference point is expressed by adirectional channel impulse response vector of Equation (2) belowrepresenting W path channels.h _(d) ^((k,k) ^(d) ⁾=(h _(d,1) ^((k,k) ^(d) ⁾,h _(d,2) ^((k,k) ^(d) ⁾,. . . ,h _(d,W) ^((k,k) ^(d) ⁾, k=1 . . . K,k_(d)=1 . . . K_(d) ^((k))  Equation (2)where a superscript ‘T’ denotes transpose of a matrix or a vector, andan underline indicates a matrix or a vector.

For each antenna element k_(a), W path channels associated with each ofa total of K users are measured. Using Equation (1) and Equation (2), itis possible to calculate a discrete-time channel impulse response vectorrepresentative of a channel characteristic for an antenna k_(a) for auser #k as shown in Equation (3). $\begin{matrix}{\begin{matrix}{{\underset{\_}{h}}^{({k,k_{a}})} = \left( {{\underset{\_}{h}}_{1}^{({k,k_{a}})},{\underset{\_}{h}}_{2}^{({k,k_{a}})},\ldots\quad,{\underset{\_}{h}}_{W}^{({k,k_{a}})}} \right)^{T}} \\{{= {\sum\limits_{k_{d} = 1}^{K_{d}^{k}}{\exp{\left\{ {{j\Psi}\left( {k,k_{a},k_{d}} \right)} \right\} \cdot {\underset{\_}{h}}_{d}^{({k,k_{d}})}}}}},}\end{matrix}{{k = {1\quad\ldots\quad K}},{k_{a} = {1\quad\ldots\quad K_{a}}}}} & {{Equation}\quad(3)}\end{matrix}$

In Equation (3), h ^((k,k) ^(d) ⁾ denotes a vector representing adiscrete-time channel impulse response characteristic for a k_(d) ^(th)spatial direction, from a user #k. Herein, the vector indicates that thechannel impulse response characteristic includes directional channelimpulse response characteristics h ₁ ^((k,k) ^(d) ⁾,h ₂ ^((k,k) ^(d) ⁾,. . . ,h _(W) ^((k,k) ^(d) ⁾ for W spatial channels. The directionalchannel impulse response characteristics are associated with the DOAsillustrated in Equation (1).

Using a directional channel impulse response vector of Equation (5)below that uses a W×(W·K_(d) ^((k))) phase matrix illustrated inEquation (4) below including a phase factor Ψ associated with a user #kand an antenna element k_(a) and includes all directional impulseresponse vectors associated with the user #k, Equation (3) is rewrittenas Equation (6).A _(s) ^((k,k) ^(a) ⁾=(e^(jΨ(k,k) ^(a) ^(,1))I_(w),e^(jΨ(k,k) ^(a)^(,2))I_(W), . . . ,e^(jΨ(k,k) ^(a) ^(,K) ^(d) ^((k)) ⁾I_(W)), k=1 . . .K,k_(a)=1 . . . K_(a)   Equation (4)where A _(s) ^((k,k) ^(a) ⁾ denotes a phase vector for K_(d) ^((d))directions of a user #k, and I_(w) denotes a W×W identity matrix.h _(d) ^((k))=(h _(d) ^((k,1)T),h _(d) ^((k,2)T), . . . ,h _(d) ^((k,K)^(d) ^((k)) ^()T))^(T), k=1 . . . K   Equation (5)h ^((k,k) ^(a) ⁾=A _(s) ^((k,k) ^(a) ⁾ h _(d) ^((k)), k=1 . . .K,k_(a)=1 . . . K_(a)   Equation (6)

Using a channel impulse response of Equation (6) associated with a user#k, a channel impulse response vector comprised of K·W elements for anantenna element k_(a) for all of K users is written ash ^((k) ^(a) ⁾=((A _(s) ^((1,k) ^(a) ⁾ h _(d) ⁽¹⁾)^(T),(A _(s) ^((2,k)^(a) ⁾ h _(d) ⁽²⁾)^(T), . . . ,(A _(s) ^((K,k) ^(a) ⁾ h _(d)^((K)))^(T))^(T), k_(a)=1 . . . K_(a)   Equation (7)

A directional channel impulse response vector having K·W·K_(d) ^((k))elements is defined ash _(d)=(h _(d) ^((1)T),h _(d) ^((2)T), . . . ,h _(d) ^((K)T))^(T)  Equation (8)where h _(d) ^((k)) denotes a directional channel impulse responsevector for a user #k.

Equation (9) below expresses a phase matrix A _(s) ^((k) ^(a) ⁾ for allof K users for an antenna element k_(a) as a set of phase matrixes foreach user. $\begin{matrix}{{{\underset{\_}{A}}_{s}^{(k_{a})} = \begin{bmatrix}{\underset{\_}{A}}_{s}^{({1,k_{a}})} & 0 & \ldots & 0 \\0 & {\underset{\_}{A}}_{s}^{({2,k_{a}})} & \ldots & 0 \\\vdots & \vdots & ⋰ & \vdots \\0 & 0 & \ldots & {\underset{\_}{A}}_{s}^{({K,k_{a}})}\end{bmatrix}},{k_{a} = {1\quad\ldots\quad K_{a}}}} & {{Equation}\quad(9)}\end{matrix}$

In Equation (9), a ‘0’ denotes a W×(W·K_(d) ^((k))) all-zero matrix, andthe phase matrix A _(s) ^((k) ^(a) ⁾ has a size of (K·W)×(K·W·K_(d)^((k))). Then, for Equation (7), a channel impulse response vector forall of K_(d) ^((k)) signals for all of K users at an antenna elementk_(a) can be calculated byh ^((k) ^(a) ⁾=A _(s) ^((k) ^(a) ⁾ h _(d), k_(a)=1 . . . K_(a)  Equation (10)

Using Equation (10), a combined channel impulse response vector havingK·W·K_(a) elements is written ash=(h ^((1)T),h ^((2)T), . . . ,h ^((K) ^(a) ^()T))^(T)   Equation (11)

That is, a phase matrix A _(s) in which all of K_(d) ^((k)) spatialsignals for all of the K users for all of K_(a) antenna elements aretaken into consideration is defined as Equation (12), and a combinedchannel impulse response vector h is calculated by a phase matrix and adirectional channel impulse response vector as shown in Equation (13).A _(s)=A _(s) ^((1)T),A _(s) ^((2)T), . . . ,A _(s) ^((K) ^(a)^()T))^(T)   Equation (12)h=A _(s) h _(d)   Equation (13)

The phase matrix A _(s), as described above, is calculated usingβ^((k,k) ^(d) ⁾ representative of DOAs for the spatial signals for eachuser.

The directional channel impulse response vector h _(d) includes theinfluence of interference power and noise. The possible number ofinterferences incident upon a receiver is expressed asL=L _(m) −W+1   Equation (14)where L_(m) denotes a length of a mid-amble as described above, and Wdenotes the number of path channels.

When K_(i) interference signals having the highest power among a totalof L noises are taken into consideration, if an angle to a referenceline estimated for a k_(i) ^(th) interference signal among the Kiinterference signals is defined as an incident angle γ^((k) ^(i) ⁾ ofthe corresponding interference signal, a phase factor of a k_(i) ^(th)interference signal incident upon a k_(a) ^(th) antenna element iswritten as $\begin{matrix}{{{\Phi\left( {k_{i},k_{a}} \right)} = {2\pi\quad{\frac{l^{(k_{a})}}{\lambda} \cdot {\cos\left( {\gamma^{(k_{i})} - \alpha^{(k_{a})}} \right)}}}},{k_{i} = {1\quad\ldots\quad K_{i}}},{k_{a} = {1\quad\ldots\quad K_{a}}}} & {{Equation}\quad(15)}\end{matrix}$

Assuming that a reception vector associated with an interference signalk_(i) is defined as n _(i) ^((k) ^(i) ⁾, a noise vector n ^((k) ^(a) ⁾for a k_(a) ^(th) antenna element becomes $\begin{matrix}{{{\underset{\_}{n}}^{(k_{a})} = {{\sum\limits_{k_{i} = 1}^{K}{{\mathbb{e}}^{{j\Phi}{({k_{i},k_{a}})}}{\underset{\_}{n}}_{i}^{(k_{a})}}} + {\underset{\_}{n}}_{th}^{(k_{a})}}},{k_{a} = {1\quad\ldots\quad K_{a}}}} & {{Equation}\quad(16)}\end{matrix}$

In Equation (16), a vector n _(th) ^((k) ^(a) ⁾ denotes a thermal noisemeasured at an antenna element k_(a) having a double-sided spectralnoise density N_(o)/2, a lower-case letter ‘e’ denotes an exponentialfunction of a natural logarithm, and N₀ denotes spectral noise density.

However, because of spectrum forming by modulation and filtering, ameasured thermal noise is generally a non-white noise. The non-whitenoise has a thermal noise covariance matrix having a normalized temporalcovariance matrix {tilde over (R)} _(th) of a colored noise as shown inEquation (17).R _(th)=N₀ {tilde over (R)} _(th)   Equation (17)

In Equation (17), ‘{tilde over ()} (tilde)’ means an estimated value,and a description thereof will be omitted herein for convenience.

If a Kronecker symbol shown in Equation (18) below is used, an L×Lcovariance matrix R _(n) ^((u,v)) meaning noise power between an u^(th)antenna element and a v^(th) antenna element is written as Equation(19). Herein, u and v each are a natural number between 1 and K_(a).$\begin{matrix}{\delta_{uv} = \left\{ \begin{matrix}1 & {u = v} \\0 & {else}\end{matrix} \right.} & {{Equation}\quad(18)} \\\begin{matrix}{{\underset{\_}{R}}_{n}^{({u,v})} = {E\left\{ {{\underset{\_}{n}}^{(u)}{\underset{\_}{n}}^{{(v)}H}} \right\}}} \\{= {E\left\{ \left( {\sum\limits_{k_{i} = 1}^{K_{i}}{{\mathbb{e}}^{{j\Phi}{({k_{i},u})}}{\underset{\_}{n}}_{i}^{(k_{i})}{\underset{\_}{n}}_{th}^{(u)}}} \right) \right.}} \\\left. \left( {{\sum\limits_{k_{i} = 1}^{K_{i}}{{\mathbb{e}}^{{j\Phi}{({k_{i},v})}}{\underset{\_}{n}}_{i}^{(k_{i})}}} + {\underset{\_}{n}}_{th}^{(v)}} \right)^{H} \right\} \\{= {{E\left\{ \left( {\sum\limits_{k_{i} = 1}^{K_{i}}{{\mathbb{e}}^{{{j\Phi}{({k_{i},u})}} - {{j\Phi}{({k_{i},v})}}}{\underset{\_}{n}}_{i}^{(k_{i})}{\underset{\_}{n}}_{i}^{{(k_{i})}H}}} \right) \right\}} +}} \\{E\left\{ {{\underset{\_}{n}}_{th}^{(u)}{\underset{\_}{n}}_{th}^{{(v)}H}} \right\}} \\{= {{\sum\limits_{k_{i} = 1}^{K_{i}}{{\mathbb{e}}^{{{j\Phi}{({k_{i},u})}} - {{j\Phi}{({k_{i},v})}}}E\left\{ {{\underset{\_}{n}}_{i}^{(k_{i})}{\underset{\_}{n}}_{i}^{{(k_{i})}H}} \right\}}} +}} \\{{\delta_{uv}N_{0}{\overset{\sim}{\underset{\_}{R}}}_{th}},u,{v = {1\quad\ldots\quad K_{a}}}}\end{matrix} & {{Equation}\quad(19)}\end{matrix}$

In Equation (19), E{•} denotes a function for calculating energy, and asuperscript ‘H’ denotes a Hermitian transform of a matrix or a vector.Assuming in Equation (19) that interference signals of different antennaelements have no spatial correlation and there is no correlation betweenthe interferences and thermal noises, Equation (20) is given. Therefore,in accordance with Equation (20), the energy of a k_(i) ^(th)interference signal can be calculated using the power of the k_(i) ^(th)interference signal.E{n _(i) ^((k) ^(i) ⁾ n _(i) ^((k) ^(i) ^()H)}=(σ^((k) ^(i) ⁾)² ·{tildeover (R)}   Equation (20)

In Equation (20), {σ^((k) ^(i) ⁾)² denotes the power of a k_(i) ^(th)interference signal. The L×L normalized temporal covariance matrix{tilde over (R)} is constant for all of K_(i) interferences andrepresents a spectral form of an interference signal, and its value isknown to a receiver. The {tilde over (R)} is a matrix indicating acorrelation value between one interference signal and anotherinterference signal, for each of the interference signals. Thecorrelations are determined according to whether the relationshipsbetween the interference signals are independent or dependent. If thereis high probability that when one interference signal A occurs anotherinterference signal B will occur, a correlation between the twointerference signals is high. In contrast, if there is no relationbetween the generation of the two interference signals, a correlationbetween the two interference signals is low. Therefore, if there is nocorrelation between interference signals, in other words, if theinterference signals are independent, {tilde over (R)} has a form of aunit matrix in which all elements except the diagonal elements are 0s.That is, {tilde over (R)} _(th) and {tilde over (R)} are approximatelyequal to each other as shown in Equation (21) below.{tilde over (R)}≈{tilde over (R)} _(th)≈I_(L)   Equation (21)

In Equation (21), I_(L) denotes an L×L identity matrix. Thus, Equation(19) can be simplified as $\begin{matrix}\begin{matrix}{{\underset{\_}{R}}_{n}^{({u,v})} = {{\overset{\sim}{\underset{\_}{R}} \cdot {\sum\limits_{k_{i} = 1}^{K_{i}}{\left( \sigma^{(k_{i})} \right)^{2}{\mathbb{e}}^{{{j\Phi}{({k_{i},u})}} - {{j\Phi}{({k_{i},v})}}}}}} + {\delta_{uv}N_{0}{\overset{\sim}{\underset{\_}{R}}}_{th}}}} \\{= {{{\underset{\_}{r}}_{u,v}\overset{\sim}{\underset{\_}{R}}} + {\delta_{uv}N_{0}{\overset{\sim}{\underset{\_}{R}}}_{th}}}} \\{{\approx {\left( {{\underset{\_}{r}}_{u,v} + {\delta_{uv}N_{0}}} \right)I_{L}}},u,{v = {1\quad\ldots\quad K_{a}}}}\end{matrix} & {{Equation}\quad(22)}\end{matrix}$

A vector r _(u,v) is an interference signal between an antenna element‘u’ and an antenna element ‘v’, defined by Equation (22) itself.

Using Equation (22), an LK_(a)×LK_(a) covariance matrix of a combinednoise vector n defined in Equation (15) is expressed as $\begin{matrix}\begin{matrix}{{\underset{\_}{R}}_{n} = {{\begin{bmatrix}{\underset{\_}{r}}_{1,1} & {\underset{\_}{r}}_{1,2} & \ldots & {\underset{\_}{r}}_{1,K_{a}} \\{\underset{\_}{r}}_{2,1} & {\underset{\_}{r}}_{2,2} & \ldots & {\underset{\_}{r}}_{2,K_{a}} \\\vdots & \vdots & ⋰ & \vdots \\{\underset{\_}{r}}_{K_{a},1} & {\underset{\_}{r}}_{K_{a},2} & \ldots & {\underset{\_}{r}}_{K_{a},K_{a}}\end{bmatrix} \otimes \overset{\sim}{\underset{\_}{R}}} + {N_{0}{I_{K_{a}} \otimes {\overset{\sim}{\underset{\_}{R}}}_{th}}}}} \\{= {{{\underset{\_}{R}}_{DOA} \otimes \overset{\sim}{\underset{\_}{R}}} + {N_{0}{I_{K_{a}} \otimes {\overset{\sim}{\underset{\_}{R}}}_{th}}}}} \\{\approx {\left\lbrack {{\underset{\_}{R}}_{DOA} + {N_{0}I_{K_{a}}}} \right\rbrack \otimes \overset{\sim}{\underset{\_}{R}}}} \\{\approx {\left\lbrack {{\underset{\_}{R}}_{DOA} + {N_{0}I_{K_{a}}}} \right\rbrack \otimes I_{L}}}\end{matrix} & {{Equation}\quad(23)}\end{matrix}$

In Equation (23), a matrix R _(DOA) denotes interference power, and isdefined by Equation (23) itself. The matrix R _(DOA), as it issubstantially equal to the vector r _(u,v), becomes a Hermitian matrixin which the diagonal elements are equal to each other. Therefore, ifonly the upper and lower triangular elements of the R _(DOA) matrix areestimated, all of the remaining elements can be determined.

According to Equation (22) and Equation (23), it is noted that aK_(a)×K_(a) matrix R _(DOA) is related only to DOAs and the interferencepower of K_(i) interferences. Assuming that there is no spatialcorrelation between the interference signals of the different antennaelements, because the interference signals between the different antennaelements become 0, the R _(DOA) can be determined using only the k_(i)^(th) interference power {σ^((k) ^(i) ⁾)² and the spectral noise densityN₀, and the overall noise power R _(n) is calculated by the R _(DOA).

Such beam forming comprises a first step of measuring noise andinterference power that indicate an influence of noises andinterferences, a second step of measuring a spatial and temporal channelimpulse response using the measured noise and interference power, and athird step of calculating steering vectors based on the estimatedchannel impulse response and performing beam forming using the channelimpulse response and the steering vectors for an estimated DOA of anincident wave.

Estimation of DOAs is one of the important factors covering one of aplurality of steps performed to acquire a desired signal. A receiverevaluates signal characteristics for all directions of 0 to 360°, andregards a direction having a peak value as a DOA. Because this processrequires so many calculations, research is being performed on severalschemes for simplifying the DOA estimation. However, even though thereceiver achieves correct DOA estimation, it is difficult to form a beamthat correctly receives only the incident wave for a corresponding DOAaccording to the estimated DOA. Further, in order to accurately estimateDOAs, many calculations are required.

Therefore, an embodiment of the present invention replaces the irregularspatial sampling with a regular sampling technique and uses severalpredetermined fixed values instead of estimating DOAs in a beam formingprocess.

An array antenna that forms beams in several directions represented byDOAs can be construed as a spatial low-pass filter that passes only thesignals of a corresponding direction. The minimum spatial samplingfrequency is given by the maximum spatial bandwidth B of a beam former.For a single unidirectional antenna, B=1/(2π).

If a spatially periodic low-pass filtering characteristic is taken intoconsideration using given DOAs, regular spatial sampling with a finitenumber of spatial samples is possible. Essentially, the number of DOAs,representing the number of spatial samples, such as the number ofresolvable beams, is given by a fixed value N_(b). Selection of theN_(b) depends upon the array geometry. In the case of a Uniform CircularArray (UCA) antenna where antenna elements are arranged on a circularbasis, the N_(b) is selected such that it should be equal to the numberof antenna elements. In the case of another array geometry, for example,an Uniform Linear Array (ULA), the N_(b) is determined by Equation (24)so that the maximum spatial bandwidth possible that is determined forall possible scenarios can be taken into consideration.N_(b)=┌2πB┐  Equation (24)

In Equation (24), ‘┌x┐’ denotes the maximum integer not exceeding avalue “x”. For example, assuming that the possible maximum spatialbandwidth is B=12/(2π), there are N_(b)=12 beams.

In the case where the number of directions, K_(d) ^((k)) (k=1, . . . ,K), is fixed and the regular spatial sampling is implemented accordingto an embodiment of the present invention, the number K_(d) ^((k)) ofdirections is equal to the number N_(b) of DOAs. Accordingly, in thereceiver, a wave transmitted by a user #k affects the antenna array inthe N_(b) different directions. As described above, each direction isrepresented by the cardinal identifier k_(d) (k_(d)=1, . . . , N_(b)),and angles β^((k,k) ^(d) ⁾ associated with DOAs are taken from a finiteset B defined as $\begin{matrix}{B = \left\{ {\beta_{0},{\beta_{0} + \frac{2\pi}{N_{b}}},{\beta_{0} + {2\quad\frac{2\pi}{N_{b}}}},\ldots\quad,{\beta_{0} + {\left( {N_{b} - 1} \right)\frac{2\pi}{N_{b}}}}} \right\}} & {{Equation}\quad(25)}\end{matrix}$

In Equation (25), β_(o) denotes a randomly-selected fixed zero phaseangle, and is preferably set to a value between 0 and π/N_(b) [radian].In the foregoing example where N_(b)=12 beams and β_(o)=0 are used,Equation (25) calculates Equation (26) below corresponding to a set ofangles including 0°, 30°, 60°, . . . , 330°. $\begin{matrix}{B = \left\{ {0,\frac{\pi}{6},{2\quad\frac{\pi}{6}},\ldots\quad,{11\quad\frac{\pi}{6}}} \right\}} & {{Equation}\quad(26)}\end{matrix}$

When the set B of Equation (26) is selected, the possible differentvalues of β^((k,k) ^(d) ⁾ are the same for all users k=1, . . . , K. Thevalues are previously known to the receiver. Therefore, the receiver nolonger requires the DOA estimation.

Assuming that there are K_(i)=N_(b) interferences, implementation ofangle domain sampling will be described in more detail below. Becauseall of the possible values of Equation (26) are acquired by anglesβ^((k,k) ^(d) ⁾ of incident signals and angles γ^((k) ^(i) ⁾ ofinterference signals, the β^((k,k) ^(d) ⁾ and γ^((k) ^(i) ⁾ are selectedby Equation (27) and Equation (28), respectively. $\begin{matrix}{{\beta^{({k,k_{d}})} = {\beta^{(k_{d})} = {\beta_{0} + {2\quad\frac{\pi}{N_{b}}\left( {k_{d} - 1} \right)}}}},{k = {1\quad\ldots\quad K}},{k_{d} = {1\quad\ldots\quad N_{b}}}} & {{Equation}\quad(27)} \\{{\gamma^{(k_{i})} = {\beta_{0} + {2\quad\frac{\pi}{N_{b}}\left( {k_{i} - 1} \right)}}},{k_{i} = {1\quad\ldots\quad N_{b}}}} & {{Equation}\quad(28)}\end{matrix}$

From the β^((k,k) ^(d) ⁾ and γ^((k) ^(i) ⁾, a phase factor of a k_(d)^(th) spatial signal, which is incident upon a k_(a) ^(th) antennaelement (k_(a)=1, . . . , K_(a)) from a k^(th) user, and a phase factorof a k_(i) ^(th) interference signal, which is incident upon the k_(a)^(th) antenna element, are calculated by Equation (29). $\begin{matrix}{{{\Psi\left( {k,k_{a},k_{d}} \right)} = {{\Psi\left( {k_{a},k_{d}} \right)} = {2\pi\quad{\frac{l^{(k_{a})}}{\lambda} \cdot {\cos\left( {\beta^{(k_{d})} - \alpha^{(k_{a})}} \right)}}}}},{{\Phi\left( {k_{i},k_{a}} \right)} = {{\Phi\left( {k_{d},k_{a}} \right)} = {2\pi\quad{\frac{l^{(k_{a})}}{\lambda} \cdot {\cos\left( {\gamma^{(k_{d})} - \alpha^{(k_{a})}} \right)}}}}},{k_{i} = {k_{d} = {1\quad\ldots\quad N_{b}}}},{k_{a} = {1\quad\ldots\quad K_{a}}},{k = {1\quad\ldots\quad K}}} & {{Equation}\quad(29)}\end{matrix}$

Herein, an angle α^((k) ^(a) ⁾ and a distance l^((k) ^(a) ⁾ are fixed bythe geometry of the array antenna.

The number of columns in the phase vector A _(s) defined in Equation(12) is K·W·K_(d) ^((k)). However, if Equation (25) and Equation (29)are used, the number of columns is fixed, thereby simplifying the signalprocessing.

Another important factor that should be performed for beam forming isestimation of the interference power R _(DOA). For the estimation of theinterference power, the typical system requires a difference signalbetween a previously received signal and a currently received signal.However, this requires a reconfiguration process for the data detectedafter being received, thereby increasing the structural complexity ofthe receiver.

FIG. 3 is a block diagram illustrating a structure of a receiver in anarray antenna system according to an embodiment of the presentinvention. Referring to FIG. 3, an antenna 110 is an array antennahaving antenna elements in a predetermined geometry, and receives aplurality of spatial signals which are incident thereupon throughspaces. Each of the multipliers 120 multiplies an output of itsassociated antenna element by a weight vector determined by a beamforming operation. The received signals including the weight vector areprovided in common to a channel estimator 130, a data detector 140, andan interference and noise estimator 150.

The interference and noise estimator 150 first sets interference andnoise power to an initial value, and henceforth, measures interferenceand noise power using a difference signal between a previous receptionsignal and a current reception signal, provided from a difference signalgenerator 190. The channel estimator 130 calculates a spatial andtemporal channel impulse response matrix using the interference andnoise power. The data detector 140 detects data from the currentreception signal using the spatial and temporal channel impulse responsematrix and the interference and noise power, and the detected data issubject to error correction and decoding by a decoder 160.

The decoded data is encoded again by an encoder 170, to be used forinterference and noise estimation. A reception signal reconfigurer 180reconfigures the previous reception signal using the coded data, andprovides the reconfigured previous reception signal to the differencesignal generator 190 such that it can be compared with the currentreception signal. In this way, the interference and noise estimator 150compares the previous reception signal subjected to FEC decoding withthe current reception signal, and uses the comparison result forestimation of interference power.

However, the encoding and reception signal reconfiguration processincreases structural complexity of the receiver and causes a delay inthe estimation of the interference power. In the following description,therefore, an exemplary embodiment of the present invention provides asimpler algorithm to reduce the implementation complexity of theprocess.

A description will now be made of a least square beam forming processaccording to an embodiment of the present invention. A jointtransmission paradigm considered in an embodiment of the presentinvention will first be described in detail with mathematicalexpressions.

As described above, the number of data symbols in a half burst and thenumber of OVSF code chips per data symbol will be denoted by N and Q,respectively. If the number of users is defined as K, a combined datavector having K·N data symbols is denoted by d. Assuming that spreadingby an OVSF code and passing through a radio channel are represented by asystem matrix A, a reception vector is given ase=Ad+n   Equation (30)

The system matrix is expressed as Equation (31) using an OVSF code C^((k)) allocated to a user #k and a channel impulse response matrix h^((k)) for the user #k.A ^((k))=h ^((k)) C ^((k))   Equation (31)

In the case of an unknown R _(DOA), a data vector can be estimatedthrough Equation (32) using a known spatio-temporal zero forcing blocklinear equalizer (ZF-BLE) method for joint detection of transmitteddata.{circumflex over (d)}≈[A ^(H)(I_(K) _(a) {circle over (x)}{tilde over(R)} ⁻¹)A]⁻¹ A ^(H)(I_(K) _(a) {circle over (x)}{tilde over (R)}⁻¹)e  Equation (32)

In Equation (32), {tilde over (R)} is a value previously known to thereceiver, and I_(K) _(a) is a K_(a)×K_(a) identity matrix. In the caseof a low bit error rate (BER), a quantized version Q{{circumflex over(d)}} of the data vector is equal to a true data vector, such as{circumflex over (d)} _(q)=Q{{circumflex over (d)}}  Equation (33)

A noise at the ZF-BLE is given byn′=e−A{circumflex over (d)} _(q)   Equation (34)

In order to calculate a spatial covariance matrix R _(DOA) ofinterferences, an expected value for the number of estimated datasamples in a cell must be known. However, because the number of theestimated data samples is infinite, it is impossible to know theexpected value in the actual system. Therefore, the preferred embodimentof the present invention acquires R _(DOA) from continuously receivedvectors.

It is assumed that an interference scenario is in a rather stationarystate such that the spatial covariance matrix of interferences can beestimated. Essentially, this means that adjacent cells are rathertightly synchronized without using slot frequency hopping.

A superscript ‘z’ is added to the noise vector of Equation (34) to bedistinguished from its preceding and succeeding noise vectors, and it isconsidered that the ‘z’ ranges from 1 to Z. The Z is preferably selectedto be less than N. Then, a noise vector estimated from an antennaelement k_(a) (k_(a)=1, . . . , K_(a)) is denoted by {circumflex over(n)} _(W) ^((K) ^(a) ^(,z)) having preferably 2(NQ+W−1) elements. Thenumber of data symbols in each half burst and the number of chips perdata symbol are determined as N and Q, respectively. As a result, aK_(a)·Z×2(NQ+W−1) noise matrix representing Z noises at all the antennaelements is $\begin{matrix}{{\hat{\underset{\_}{N}}}_{DOA} = \begin{bmatrix}{\hat{\underset{\_}{n}}}^{{({1,1})}T} & {\hat{\underset{\_}{n}}}^{{({1,2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({1,Z})}T} \\{\hat{\underset{\_}{n}}}^{{({2,1})}T} & {\hat{\underset{\_}{n}}}^{{({2,2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({2,Z})}T} \\\vdots & \vdots & ⋰ & \vdots \\{\hat{\underset{\_}{n}}}^{{({K_{a},1})}T} & {\hat{\underset{\_}{n}}}^{{({K_{a},2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({K_{a},Z})}T}\end{bmatrix}} & {{Equation}\quad(35)}\end{matrix}$where a superscript ‘T’ denotes transpose.

Therefore, a K_(a)×K_(a) estimated interference power matrix of Equation(36) can be yielded by normalizing an autocorrelation matrix of thenoise matrix by Z. $\begin{matrix}{{\hat{\underset{\_}{R}}}_{DOA} = {{\frac{1}{Z}{\hat{\underset{\_}{N}}}_{DOA}{\hat{\underset{\_}{N}}}_{DOA}^{H}}\quad\quad = {\frac{1}{Z}\begin{bmatrix}{\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({1,z})}}^{2}} & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({2,z})}}} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \\\left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({2,z})}}} \right)^{*} & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({2,z})}}^{2}} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({2,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \\\vdots & \vdots & ⋰ & \vdots \\\left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \right)^{*} & \left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({2,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \right)^{*} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({K_{a},z})}}^{2}}\end{bmatrix}}}} & {{Equation}\quad(36)}\end{matrix}$

Although there is still thermal noise, given that the noise vector usedin Equation (35) is a difference between an actually received signal andan estimated data vector as shown in Equation (34), the estimatedinterference power calculated by Equation (36) becomes an estimatedvalue of R _(DOA)+N₀I_(K) _(a) in Equation (23), used in finding actualnoise and interference power. Therefore, further estimation of thethermal noise and other factors is not required.

Because the estimated interference power of Equation (36) is a Hermitianmatrix, it is needed to estimate diagonal and off-diagonal elements ofan upper or lower triangular part of {circumflex over (R)} _(DOA). Theresultant {circumflex over (R)} _(DOA) must be permanently updated at anupdate rate that depends on a change rate of the foregoing scenario.

Once Equation (36) is calculated, an estimated noise and interferencepower value can be found as shown in Equation (37) by Equation (23).{circumflex over (R)} _(n)={circumflex over (R)} _(DOA){circle over(x)}{tilde over (R)}  Equation (37)

In an approximately white noise environment, Equation (37) is simplifiedas{circumflex over (R)} _(n)≈{circumflex over (R)} _(DOA){circle over(x)}I _(L)   Equation (38)

In Equation (38), I _(L) denotes an L×L identity matrix, and theestimated interference power is extended for all of L interferencesignals by Equation (38).

FIG. 4 is a block diagram illustrating a structure of a receiver in anarray antenna system according to another embodiment of the presentinvention, and FIG. 5 is a flowchart illustrating an operation ofcalculating interference power by a data estimator 260, a quantizer 270,and an interference and noise estimator 250 in the receiver illustratedin FIG. 4.

Referring to FIG. 4, an antenna 210 is an array antenna having antennaelements in a predetermined geometry, and receives a plurality ofspatial signals which are incident thereupon through space. Each of themultipliers 220 multiplies an output of its associated antenna elementby a weight vector determined by a beam forming operation. The receivedsignals including the weight vector are provided in common to a channelestimator 230, a data detector 240, and an interference and noiseestimator 250.

The interference and noise estimator 250 first sets interference andnoise power to an initial value, and henceforth, measures interferenceand noise power using a quantized data vector provided from thequantizer 270 and a current reception signal. The channel estimator 230calculates a spatial and temporal channel impulse response matrix and asystem matrix using the interference and noise power. The data detector240 detects data from the current reception signal using the spatial andtemporal channel impulse response matrix and the interference and noisepower, and the detected data is provided to a decoder (not shown) forerror correction and decoding.

Referring to FIG. 5, in step 310, the data estimator 260 estimates adata vector by applying the interference and noise power provided fromthe interference and noise estimator 250 and the system matrix providedfrom the channel estimator 230, to a current reception signal. In step320, the quantizer 270 quantizes the estimated data vector and providesthe quantized data vector to the interference and noise estimator 250.The interference and noise estimator 250 calculates a noise vector usingEquation (34) in step 330, and calculates a noise matrix using Equation(35) in step 340. In step 350, the interference and noise estimator 250calculates the estimated interference power by normalizing anautocorrelation matrix of the noise matrix in accordance with Equation(36) by a predetermined value Z, and calculates the interference andnoise power using the estimated interference power. The interference andnoise power is used in the receiver for determining a radio channelenvironment and performing beam forming.

As can be understood from the foregoing description, the novel beamformer performs regular spatial sampling instead of estimating DOAsneeded for determining weights, and directly calculates interferencepower based on an estimated data vector instead of decoding a receiveddata vector and encoding the decoded data vector, thereby simplifyingthe structure of the receiver and reducing power measurement delay.

While the invention has been shown and described with reference to acertain exemplary embodiments thereof, it will be understood by thoseskilled in the art that various changes in form and details may be madetherein without departing from the spirit and scope of the invention asdefined by the appended claims.

1. A noise and interference power measurement apparatus for an antennadiversity system having a plurality of antenna elements, the apparatuscomprising: a channel estimator for estimating a channel impulseresponse for a radio channel corresponding to a predetermined pluralityof regularly spaced direction-of-arrival (DOA) values; a data estimatorfor estimating received data using a received signal and a system matrixincluding an allocated spreading code and the channel impulse response;a quantizer for quantizing the estimated data; and an interference andnoise calculator for calculating noise vectors at the respective antennaelements by removing from the received signal an influence of thequantized data to which the system matrix is applied, calculating anestimated noise matrix at the plurality of antenna elements, theestimated noise matrix including the noise vectors, calculatinginterference power by auto-correlating the estimated noise matrix, andcalculating noise and interference power based on the interferencepower.
 2. The noise and interference power measurement apparatus ofclaim 1, wherein the received data is estimated by{circumflex over (d)}≈[A ^(H)(I_(K) _(a) {circle over (x)}{tilde over(R)} ⁻¹)A]⁻¹ A ^(H)(I_(K) _(a) {circle over (x)}{tilde over (R)} ⁻¹)ewhere A denotes the system matrix, I_(K) _(a) denotes a K_(a)×K_(a)identity matrix, K_(a) denotes the number of the antenna elements,{tilde over (R)} denotes a predefined normalization value, and e denotesthe received signal.
 3. The noise and interference power measurementapparatus of claim 1, wherein each of the noise vectors is calculated byn′=e−A{circumflex over (d)} _(q) where e denotes the received signal, Adenotes the system matrix, and {circumflex over (d)} _(q) denotes thequantized data.
 4. The noise and interference power measurementapparatus of claim 3, wherein the noise matrix is expressed as${\hat{\underset{\_}{N}}}_{DOA} = \begin{bmatrix}{\hat{\underset{\_}{n}}}^{{({1,1})}T} & {\hat{\underset{\_}{n}}}^{{({1,2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({1,Z})}T} \\{\hat{\underset{\_}{n}}}^{{({2,1})}T} & {\hat{\underset{\_}{n}}}^{{({2,2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({2,Z})}T} \\\vdots & \vdots & ⋰ & \vdots \\{\hat{\underset{\_}{n}}}^{{({K_{a},1})}T} & {\hat{\underset{\_}{n}}}^{{({K_{a},2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({K_{a},Z})}T}\end{bmatrix}$ where {circumflex over (n)} ^((k) ^(a) ^(,z)) denotes anoise vector indicating a z^(th) noise at a k_(a) ^(th) antenna element,and Z denotes a value previously selected such that it is less than thenumber of data symbols constituting the estimated data.
 5. The noise andinterference power measurement apparatus of claim 4, wherein theinterference power is calculated by $\begin{matrix}{{\hat{\underset{\_}{R}}}_{DOA} = {\frac{1}{Z}{\hat{\underset{\_}{N}}}_{DOA}{\hat{\underset{\_}{N}}}_{DOA}^{H}}} \\{= {\frac{1}{Z}\begin{bmatrix}{\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({1,z})}}^{2}} & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({2,z})}}} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \\\left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({2,z})}}} \right)^{*} & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({2,z})}}^{2}} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({2,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \\\vdots & \vdots & ⋰ & \vdots \\\left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \right)^{*} & \left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({2,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \right)^{*} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({K_{a},z})}}^{2}}\end{bmatrix}}}\end{matrix}$ where {circumflex over (R)} _(DOA) denotes theinterference power, {circumflex over (N)} _(DOA) denotes the noisematrix, Z denotes a value previously selected such that it is less thanthe number of data symbols constituting the estimated data, and{circumflex over (n)} ^((k) ^(a) ^(,z)) denotes a noise vectorindicating a z^(th) noise at a k_(a) ^(th) antenna element.
 6. The noiseand interference power measurement apparatus of claim 5, wherein thenoise and interference power is calculated by{circumflex over (R)} _(n)={circumflex over (R)} _(DOA){circle over(x)}{tilde over (R)} where {circumflex over (R)} _(n) denotes the noiseand interference power, and {tilde over (R)} denotes a predefinednormalization value.
 7. The noise and interference power measurementapparatus of claim 5, wherein the noise and interference power iscalculated by{circumflex over (R)} _(n)≈{circumflex over (R)} _(DOA){circle over(x)}I _(L) where {circumflex over (R)} _(n) denotes the noise andinterference power, I _(L) denotes an L×L identity matrix, and L denotesa predetermined number of interference signals.
 8. A noise andinterference power measurement method for an antenna diversity systemhaving a plurality of antenna elements, the method comprising the stepsof: estimating a channel impulse response for a radio channelcorresponding to a predetermined plurality of regularly spaceddirection-of-arrival (DOA) values; estimating received data using areceived signal and a system matrix including an allocated spreadingcode and the channel impulse response; quantizing the estimated data;calculating noise vectors at the respective antenna elements by removingfrom the received signal an influence of the quantized data to which thesystem matrix is applied; calculating an estimated noise matrix at theplurality of antenna elements, the estimated noise matrix including thenoise vectors, and calculating interference power by auto-correlatingthe estimated noise matrix; and calculating noise and interference powerbased on the interference power.
 9. The noise and interference powermeasurement method of claim 8, wherein the received data is estimated by{circumflex over (d)}≈[A ^(H)(I_(K) _(a) {circle over (x)}{tilde over(R)} ⁻¹)A]⁻¹ A ^(H)(I_(K) _(a) {circle over (x)}{tilde over (R)} ⁻¹)ewhere A denotes the system matrix, I_(K) _(a) denotes a K_(a)×K_(a)identity matrix, K_(a) denotes the number of the antenna elements,{tilde over (R)} denotes a predefined normalization value, and e denotesthe received signal.
 10. The noise and interference power measurementmethod of claim 8, wherein each of the noise vectors is calculated byn′=e−A{circumflex over (d)} _(q) where e denotes the received signal, Adenotes the system matrix, and {circumflex over (d)} _(q) denotes thequantized data.
 11. The noise and interference power measurement methodof claim 10, wherein the noise matrix is expressed as${\hat{\underset{\_}{N}}}_{DOA} = \begin{bmatrix}{\hat{\underset{\_}{n}}}^{{({1,1})}T} & {\hat{\underset{\_}{n}}}^{{({1,2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({1,Z})}T} \\{\hat{\underset{\_}{n}}}^{{({2,1})}T} & {\hat{\underset{\_}{n}}}^{{({2,2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({2,Z})}T} \\\vdots & \vdots & ⋰ & \vdots \\{\hat{\underset{\_}{n}}}^{{({K_{a},1})}T} & {\hat{\underset{\_}{n}}}^{{({K_{a},2})}T} & \ldots & {\hat{\underset{\_}{n}}}^{{({K_{a},Z})}T}\end{bmatrix}$ where {circumflex over (n)} ^((k) ^(a) ^(,z)) denotes anoise vector indicating a z^(th) noise at a k_(a) ^(th) antenna element,and Z denotes a value previously selected such that it is less than thenumber of data symbols constituting the estimated data.
 12. The noiseand interference power measurement method of claim 11, wherein theinterference power is calculated by $\begin{matrix}{{\hat{\underset{\_}{R}}}_{DOA} = {\frac{1}{Z}{\hat{\underset{\_}{N}}}_{DOA}{\hat{\underset{\_}{N}}}_{DOA}^{H}}} \\{= {\frac{1}{Z}\begin{bmatrix}{\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({1,z})}}^{2}} & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({2,z})}}} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \\\left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({2,z})}}} \right)^{*} & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({2,z})}}^{2}} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({2,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \\\vdots & \vdots & ⋰ & \vdots \\\left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({1,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \right)^{*} & \left( {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{{({2,z})}H}{\hat{\underset{\_}{n}}}^{({K_{a},z})}}} \right)^{*} & \ldots & {\sum\limits_{z = 1}^{Z}{{\hat{\underset{\_}{n}}}^{({K_{a},z})}}^{2}}\end{bmatrix}}}\end{matrix}$ where {circumflex over (R)} _(DOA) denotes theinterference power, {circumflex over (N)} _(DOA) denotes the noisematrix, Z denotes a value previously selected such that it is less thanthe number of data symbols constituting the estimated data, and{circumflex over (n)} ^((k) ^(a) ^(,z)) denotes a noise vectorindicating a z^(th) noise at a k_(a) ^(th) antenna element.
 13. Thenoise and interference power measurement method of claim 12, wherein thenoise and interference power is calculated by{circumflex over (R)} _(n)={circumflex over (R)} _(DOA){circle over(x)}{tilde over (R)} where {circumflex over (R)} _(n) denotes the noiseand interference power, and {tilde over (R)} denotes a predefinednormalization value.
 14. The noise and interference power measurementmethod of claim 12, wherein the noise and interference power iscalculated by{circumflex over (R)} _(n)≈{circumflex over (R)} _(DOA){circle over(x)}I _(L) where {circumflex over (R)} _(n) denotes the noise andinterference power, I _(L) denotes an L×L identity matrix, and L denotesa predetermined number of interference signals.